sigex.lpmse: Computes signal extraction mse arising from LP filtering of...

In jlivsey/sigex: Ecce Signum: Multivariate Signal Extraction

Computes signal extraction mse arising from LP filtering of trend-cycle, with low pass cutoff, for trend and cycle each.

Description

Background: A sigex model consists of process x = sum y, for stochastic components y. Each component process y_t is either stationary or is reduced to stationarity by application of a differencing polynomial delta(B), i.e. w_t = delta(B) y_t is stationary. We have a model for each w_t process, and can compute its autocovariance function (acf), and denote its autocovariance generating function (acgf) via gamma_w (B). The signal extraction filter for y_t is determined from this acgf and delta. The error spectral density calculations are found in: "Casting Vector Time Series: Algorithms for Forecasting, Imputation, and Signal Extraction," McElroy (2018). param is the name for the model parameters entered into a list object with a more intuitive structure, whereas psi refers to a vector of real numbers containing all hyper-parameters (i.e., reals mapped bijectively to the parameter manifold)

Usage

sigex.lpmse(param, mdl, trendcyclecomp, sigcomps, grid, cutoff)

Arguments

param	model parameters entered into a list object with an intuitive structure.
mdl	The specified sigex model, a list object
trendcyclecomp	The (single) index of the trend-cycle component
sigcomps	Provides indices of a desired component that is disjoint from trend-cycle, so that MSEs of trend+sigcomps and cycle+sigcomps are computed. (Pass in sigcomps = NULL to just get trend and cycle MSEs.)
grid	Desired number of frequencies for spectrum calculations
cutoff	A number between 0 and pi, with all frequencies < cutoff preserved

Value

list object with mse.trend and mse.cycle mse.trend: N x N matrix, MSE of trend mse.cycle: N x N matrix, MSE of cycle

jlivsey/sigex documentation built on March 20, 2024, 3:17 a.m.